<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aouag, Hichem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Étude, mise en œuvre et adaptabilité des outils de l&amp;rsquo;amélioration continue dans une industrie algérienne : Approches Théorique et Pratique</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Habilitation Universitaire</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adel Abdelhadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vers une Approche des Systèmes Multi-Agents et Méthodes d&amp;rsquo;Emergence pour la Maintenance Systématique</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ourlis, Lazhar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimisation Des Techniques de Sécurisation Du Logiciel Via l&amp;rsquo;Analyse des Codes Malveillants</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1922/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	De nos jours, la plupart des moteurs d’analyse de solutions anti-malware sont heuristiques. Ils classent les objets, les flux de données ainsi que les zones mémoires comme bénins ou malveillants en fonction de leur comportement. La plupart des fabricants d'antivirus reconnaissent que l'approche heuristique permet d'atteindre jusqu'à 90% d'efficacité en termes de taux de détection, mais consomme davantage de ressources systèmes : de tels moteurs anti-malware, disponibles en open-source, sont extrêmement inefficaces en termes d’utilisation de ressources système car ils font souvent appel à des algorithmes d'apprentissage automatique. Pour réduire cette charge système, il est fortement recommandé d'utiliser la détection par signature statique, qui permet de filtrer à elle seule la majorité des échantillons de programmes malveillants connus, en conjonction avec les technologies de détection heuristiques et celles basées sur le cloud. Dans ce travail, nous présentons un scanner de signatures rapide pour la détection de programmes malveillants, basé sur une version améliorée de l’algorithme Aho-Corasick pour la recherche de chaînes de caractères (ou motifs), conçue pour pouvoir bénéficier des techniques de vectorisation qui ajoutent une forme de parallélisme de données au code de l’algorithme. La solution proposée est implémentée en utilisant le jeu d’instructions d’Intel® Advanced Vector Extensions (AVX2).
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat en sciences  </style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zerari, Naima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">INTÉGRATION D&amp;rsquo;UN MODULE DE RECONNAISSANCE DE LA PAROLE AU NIVEAU D&amp;rsquo;UN SYSTÈME AUDIOVISUEL - APPLICATION TÉLÉVISEUR</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1918/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Cette thèse propose de concevoir et réaliser un système de reconnaissance automatique de la parole destiné à commander à distance un système audiovisuel à savoir : un Téléviseur. Le système global &quot;bout en bout&quot; se scinde en deux blocs : le premier cherche à extraire les meilleures caractéristiques à partir du signal vocal d’entrée. A cet effet, plusieurs techniques d’extraction de caractéristiques vont être examinées et testées. Concernant le deuxième bloc, nous mettons en évidence une multitude de techniques relevant du domaine de l’apprentissage profond, dont l’impact est d’adapter et de d’affirmer les caractéristiques extraites pour donner en final la classe de l’énoncé. La validation des différentes méthodologies présentées dans cette thèse a été effectuée sur la base de deux jeux de données réelles, le premier est tenu compte pour une évaluation initiale, tandis que le second est conçu exclusivement pour le système ASR proposé dans cette thèse. Les résultats obtenus ont certifié l’efficience des approches proposées. Le défi pour les travaux futurs est d’évaluer ce type de système dans des conditions plus réalistes avec des signaux vocaux issus des milieux bruités.
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat en sciences  </style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Titah, Mawloud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Amélioration du processus de capitalisation et de partage des connaissances pour la maximisation de la valeur d&amp;#39;un système de production</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1944/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Dans cette thèse, nous nous sommes intéressés à un modèle de gestion des connaissances des entreprises industrielles. Certaines tâches manufacturières impliquent un niveau élevé de connaissance tacite des opérateurs qualifiés. L'industrie a besoin des méthodes fiables pour la capture et l'analyse de ces connaissances tacites afin qu'elles puissent être partagées et sans aucune perte. Nous proposons, un modèle de gestion contenant deux processus de gestion, le premier processus est la capitalisation des connaissances basée sur une tâche industrielle. Nous avons utilisé une combinaison de deux méthodologies : une méthodologie d’ingénierie de connaissances CommonKADS et une méthodologie d’élicitation des connaissances MACTAK. Dans la phase de modélisation, nous avons utilisé deux différentes techniques de modélisation, une modélisation basée sur les connaissances d’expert et la deuxième une représentation ontologique. Ce modèle facilite la capture des connaissances d’experts et transforme les connaissances tacites en explicites avec une maximisation des règles de production. Le deuxième processus concerne le partage des connaissances à base d’une ontologie des Tâches Manufacturières MATO en identifiant un ensemble des concepts de fabrication et leurs relations, cette ontologie proposée facilite le partage des connaissances entre les tâches de fabrication et aide à partager et à réutiliser les connaissances durant l'exécution des tâches. Ensuite, une application proposée pour le diagnostic de système d’alarme dans une centrale thermique a été présentée pour démontrer l’importance et l’apport de l’ontologie.
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat en sciences  </style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bencherif, Fateh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modélisation de projet de conception de produit pour atténuer les risques dans les processus de développement: Étude et analyse de la performance industrielle </style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Doctorat en sciences  </style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Derdour, Khedidja</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Un Système de Reconnaissance de Formes Basé sur une Approche Multi-Classifieurs</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1905/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Améliorer la performance d’un système de reconnaissance de formes fait l’objet de recherche dans de nombreuses disciplines. On obtient cette amélioration par l’optimisation dans les différents partis du système de reconnaissance de formes : les prétraitements, l’extraction des paramètres caractéristique (primitives), la classification. Les travaux de recherche présentés dans cette thèse abordent le problème de la reconnaissance des chiffres arabe imprimés et manuscrits. L’objectif principal de ce travail est l’amélioration de la performance en terme de taux de reconnaissance par l'application d’un système multi classificateur (MCS). Différents classifieurs sont utilisés (KPPV, PMC, SVM, LDA, Arbre de décision, Naïve Bayesien, Pseudo inverse), à l’aide de différents types de vecteurs de caractéristiques extraite de l'image. Enfin, comme les performances de MCS dépendent des performances des classifieurs appliquée (les performances individuelles des classifieurs), et comme la performance d’un classifieur dépend ainsi des caractéristiques utilisé l'optimisation de l’extractions des primitives pertinentes est également abordée dans la thèse. Nous avons réalisé plusieurs simulations pour éprouver les classifications, en introduisant des améliorations dans les paramètres caractéristiques et en faisant des combinaisons de classifieurs. Dans une première partie, nous montrons l’intérêt de l’utilisation des paramètres caractéristique pertinente, à l’aide des classifieur individuelle (séparé, indépendants) comme source d’inspiration pour la conception de nouveaux paramètres. Nous proposons en particulier une amélioration de primitives pour la caractérisation des chiffres. On montre qu’il est possible de développer une solution efficace, à moindre coût en terme de réduction de vecteur caractéristique et transformation géométrique. Donc, Le système développé s’articule autour de quatre modules distincts. Un module de prétraitements, un d’extraction des paramètres caractéristique, un module de reconnaissance (classification) et un module de combinaison de classifieurs. Ce dernier est chargé de fusionner les sorties (décisions) de chaque classifieur basant sur des méthodes (règles) de fusion. Les résultats obtenus sur les bases des chiffres imprimés et la base MNIST des chiffres manuscrits sont prometteurs. Cette thèse apporte quelques contributions pour faire avancer notre compréhension dans ce domaine de recherche en pleine expansion
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat en sciences  </style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ghrieb, Abdel-Ouahab</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supervision de Robot Manipulateur virtuel par Les réseaux de neurones et les réseaux de Petri</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.theses-algerie.com/2211862124311993/these-de-doctorat/universite-mustapha-ben-boulaid---batna-2/supervision-d-un-robot-manipulateur-virtuel-par-les-r%C3%A9seaux-de-neurones</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Dans ce travail de thèse, nous avons proposé un système de supervision appliqué sur un robot manipulateur à deux degrés de liberté. La supervision est utilisée pour assurer la reconfiguration en temps réel du robot. Dans ce système nous avons utilisé une nouvelle méthode de détection de défaut (FD) de frottement visqueux du robot supervisé combinée avec un module de commande tolérante aux défauts (FTC).Le premier module, basé sur une méthode de traitement appliquée sur des résidus, va permettre la détection de défaut pour bien estimer les corrections nécessaires du deuxième module. Une évaluation de l’effet de défaut durant la supervision a été faite. Par ailleurs, le protocole TCP pour le transfert des données entre le robot superviseur et le robot supervisé a été utilisé. Les résultats de simulation montrent que la méthode proposée corrige l’effet de défaut en utilisant les données qui arrivent d’un robot superviseur à distance. Ensuite, nous avons proposé une implémentation matérielle sur cible FPGA de l’algorithme de supervision dont le but est de valider notre contribution et d’assurer un traitement en temps réel dans le cas où il y a des robots réels. Par ailleurs, une étude comparative entre les performances des deux implémentations a été effectuée
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat LMD</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Soltani, Mohyeddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implémentation et déploiement d&amp;rsquo;une nouvelle approche à base de Lean Six Sigma pour le développement et l&amp;rsquo;amélioration de la durabilité de la production des petites et moyennes entreprises</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1941/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	L’objectif de ce travail de thèse et de développer des nouvelles approches permettant aux petites et moyennes entreprises d’améliorer les performances de leur processus de fabrication. Nous avons développé trois approches aspirées du Lean Six Sigma (LSS) pour l’amélioration de la production dans un contexte conventionnel et classique d'une part et d'autre part dans un contexte de production durable. Dans la première approche nous avons proposé une approche Lean Six Sigma conventionnelle pour évaluer et suivre la compétitivité d’une PME en fonction des résultats obtenus par la méthode VSM. Dans la deuxième approche, nous avons proposé une nouvelle extension de l’approche LSS vers le contexte de la production durable en incorporant des algorithmes multicritères quantitatives. Cette approche nous a permis de surmonter quelques barrières au niveau du processus de l’application du LSS. Dans La troisième approche nous avons présenté une amélioration de l’approche LSS qui vise à montrer l’effet positif des algorithmes multicritères qualitatives flous pour surmonter certaines barrières du Lean Six Sigma liées aux phases d’analyse et d’amélioration de l’état actuel des processus de fabrication. Les approches proposées sont appliquées dans deux entreprise algériennes pour améliorer et contrôler la durabilité de leurs processus de fabrication.
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat LMD</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benaggoune Khaled</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pronostic industriel distribué des systèmes complexes à base d&amp;#39;agents</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1964/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Les systèmes industriels deviennent plus complexes en raison, notamment, de leur taille croissante et de l’intégration des nouvelles technologies. En vieillissant, ces systèmes deviennent plus vulnérables aux défaillances et leurs activités de maintenance sont difficiles et coûteuses. Cette situation, combinée aux exigences de productivité, de croissance des bénéfices, de disponibilité opérationnelle et de sécurité, pousse les praticiens et les chercheurs à développer des outils et des méthodes innovants. Les activités de maintenance constituent l’un des leviers possibles. En entretenant le système, nous pouvons réduire les coûts globaux de son cycle de vie, augmenter sa disponibilité, améliorer la sécurité des opérateurs et réduire les incidents environnementaux. Les tâches de maintenance peuvent être soit curatives, soit préventives. Cependant, cela n’est pas encore suffisant parce que les pièces de rechange ne sont pas disponibles ou pas suffisantes au moment de la panne ou simplement parce que les ressources nécessaires (les responsables de la maintenance) sont occupées. Une &quot;meilleure&quot; solution pourrait alors être une maintenance prédictive, qui peut être effectuée dans le framework du pronostic. Dans ce cadre nous essayons de prévoir l’état de santé du système, puis on planifie les actions appropriées en fonction des résultats des prévisions. Ce travail s’inscrit dans au domaine du pronostic de la PHM et de la gestion de la santé. Les techniques de PHM visent à prédire la durée de vie restante de l’équipement. Cependant, elles ont eu tendance à être utilisées dans un contexte local avec une intégration limitée des solutions distribuées. Dans les systèmes complexes, le comportement émergent est plus compliqué que la somme des comportements de leurs parties constitutives. Ce comportement implique la propagation de défauts entre les parties et nécessite des informations sur la façon dont les parties sont liées. Dans cette thèse, nous proposons une approche multi-agent pour la prédiction de la RUL au niveau du système. Ensuite, l’approche proposée est étendue à la PHM médicale avec une étude de cas
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat LMD</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proposition d&amp;rsquo;un système distribué de diagnostic et pronostic basé sur les services Web et Extrême Learning Machine</style></title><secondary-title><style face="normal" font="default" size="100%">Génie industriel</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://eprints.univ-batna2.dz/1915/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Cette thèse traite de l'utilisation d'un outil de Machine Learning appelé &quot;Extreme Learning Machine&quot; dans le domaine du pronostic et de l'évaluation de la santé (e.g. estimation de la durée de vie utile restante (RUL)). Cet algorithme implique des méthodes de programmation linéaires qui reposent principalement sur les moindres carrés dans les paradigmes non linéaires des réseaux de neurones artificiels pour produire des estimateurs de santé très rapides et précis. D'autres parts, cette étude vise à distribuer le système de prédiction sur le web à l'aide de services Web pour résoudre les problèmes de répartition géographique de surveillance décentralisée. Sur la base de ce nouvel outil, de nombreux algorithmes d'apprentissage ont été développés dans le cadre de ce travail et comparés à d'autres algorithmes présents dans la littérature en termes de temps et de précision. La plupart des algorithmes développés sont inspirés des théories récentes de Deep Learning et ce en raison de leur bonne réputation. Les données étudiées dans ce travail de recherche sont tirées du logiciel C-MAPSS simulateur de système de propulsion aérodynamique développé par la NASA. Les résultats obtenus ont prouvé l'efficacité des nouveaux algorithmes et les recommandent pour une utilisation future dans le domaine de l'évaluation de la santé.
&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Doctorat LMD</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baguigui, S</style></author><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author><author><style face="normal" font="default" size="100%">Habchi, A-S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monitoring The Product Quality Using The Iiot Data</style></title><secondary-title><style face="normal" font="default" size="100%">First International Conference On Energy, Thermofluids And Materials Engineering, ICETME 2021 Held Online From 18 To 20 December, 2021.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%"> Biskra Algerie</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samia Aitouche</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">knowledge sharing via the blockchain technology</style></title><secondary-title><style face="normal" font="default" size="100%">EKNOW 2021,</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%"> Nice, France</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bensakhria, Mohamed</style></author><author><style face="normal" font="default" size="100%">Samir Abdelhamid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hybrid Heuristic Optimization of an Integrated Production Distribution System with Stock and Transportation Costs</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Computing Systems and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-69418-0_11</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%"> Lecture Notes in Networks and Systems book series </style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper we address the integration of two-level supply chain with multiple items, production facility and retailers’ demand over a considered discrete time horizon. This two-level production distribution system features capacitated production facility supplying several retailers located in the same region. If production does take place, this process incurs a fixed setup cost as well as unit production costs. In addition, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles and routing costs are incurred. This work aims at implementing a solution to minimize the sum of the costs at the production facility and the retailers. The methodology adopted to tackle this issue is based on a hybrid heuristic, greedy and genetic algorithms that uses strong formulation to provide a good solution of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadjidj, Nadjiha </style></author><author><style face="normal" font="default" size="100%">Meriem Benbrahim</style></author><author><style face="normal" font="default" size="100%">Ounnas, Djamel</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES'21) </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%">Adrar, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baguigui, S</style></author><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author><author><style face="normal" font="default" size="100%">Habchi, A-S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monitoring The Product Quality Using The Iiot Data First International Conference On Energy, Thermofluids And Materials Engineering</style></title><secondary-title><style face="normal" font="default" size="100%">First International Conference On Energy, Thermofluids And Materials Engineering (ICETME 2021), Held Online From 18 To 20 December, </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%">Biskra, Algerie</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Al Masry, Zeina</style></author><author><style face="normal" font="default" size="100%">Terrissa, Sadek-Labib</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Digital Twins Driven Breast Cancer Detection</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lecture Notes in Networks and Systems </style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benaicha Sonia</style></author><author><style face="normal" font="default" size="100%">Zermane, Hannane</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Bencherif, Fateh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an Industrial Application with Neuro-Fuzzy Systems</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.naun.org/main/NAUN/fuzzy/2021/a062017-003(2021).pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, our objective is dedicated to the detection of a deterioration in the estimated operating time by giving preventive action before a failure, and the classification of breakdowns after failure by giving the action of the diagnosis and / or maintenance. For this reason, we propose a new Neuro-fuzzy assistance prognosis system based on pattern recognition called &quot;NFPROG&quot; (Neuro Fuzzy Prognosis). NFPROG is an interactive simulation software, developed within the Laboratory of Automation and Production (LAP) -University of Batna, Algeria. It is a four-layer fuzzy preceptor whose architecture is based on Elman neural networks. This system is applied to the cement manufacturing process (cooking process) to the cement manufacturing company of Ain-Touta-Batna, Algeria. And since this company has an installation and configuration S7-400 of Siemens PLC PCS7was chosen as a programming language platform for our system.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanane Zermane</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Sonia Benaicha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automation and fuzzy control of a manufacturing system</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.naun.org/main/NAUN/fuzzy/2021/a082017-004(2021).pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	The automation of manufacturing systems is a major obligation to the developments because of exponential industrial equipment, and programming tools, so that growth needs and customer requirements. This automation is achieved in our work through the application programming tools from Siemens, which are PCS 7 (Process Control System) for industrial process control and FuzzyControl++ for fuzzy control. An industrial application is designed, developed and implemented in the cement factory in Ain-Touta (S.CIM.AT) located in the province of Batna, East of Algeria. Especially in the cement mill which gives the final product that is the cement.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zemouri, Nahed</style></author><author><style face="normal" font="default" size="100%">Bouzgou, Hassen</style></author><author><style face="normal" font="default" size="100%">Gueymard,Christian A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sample Entropy with One-Stage Variational Mode Decomposition for Hourly Solar Irradiance Forecasting</style></title><secondary-title><style face="normal" font="default" size="100%">The First International Conference on Renewable  Energy Advanced Technologies and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA'21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Adrar, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Solar radiation forecasting is an important technology that is necessary to increase the performance, management, and control of modern electrical grids. It allows energy regulators to estimate the near-future output power of solar power plants, and can help to reduce the effects of power fluctuations on the electricity grid, thus increasing the overall efficiency and power quality of those plants [1]. However, the variable nature of solar irradiance poses a challenge in the exploitation of solar energy. In this context, forecasting techniques are now essential to ensure sustainable, reliable, and cost-effective solar energy production [2]. This paper proposes a hybrid machine learning model to forecast Global Horizontal Irradiance (GHI) in the short term (1-hour ahead). The experimental assessment of the model is done on the basis of an experimental dataset of 11 years of hourly GHI measurements from the BSRN Tamanrasset station in Algeria. The general framework of the proposed model is explained in Figure 1, and its main steps are summarized as follows:
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Atmani, Hanane</style></author><author><style face="normal" font="default" size="100%">Bouzgou, Hassen</style></author><author><style face="normal" font="default" size="100%">Gueymard,Christian A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Long Short-Term Memory with Separation Models for Direct Normal Irradiance Forecasting: Application to Tamanrasset, Algeria</style></title><secondary-title><style face="normal" font="default" size="100%">The First International Conference on Renewable  Energy Advanced Technologies and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA'21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Adrar, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Solar energy is a vast and clean resource that can be harnessed with great benefit for humankind. It is still currently difficult, however, to convert it into electricity in an efficient and cost-effective way. One of the ways to produce energy is the use of various focusing technologies that concentrate the direct normal irradiance (DNI) to produce power through highly-efficient modules or conventional turbines. Concentrating technologies have great potential over arid areas, such as Northern Africa. A serious issue is that DNI can vary rapidly under broken-cloud conditions, which complicate its forecasts [1]. In comparison, the global horizontal irradiance (GHI) is much less sensitive to cloudiness. As an alternative to the direct DNI forecasting avenue, a possibility exists to derive the future DNI indirectly by forecasting GHI first, and then use a conventional separation model to derive DNI. In this context, the present study compares four of the most well-known separation models of the literature and evaluates their performance at Tamanrasset, Algeria, when used in combination with a new deep learning machine methodology introduced here to forecast GHI time series for short-term horizons (15-min). The proposed forecast system is composed of two separate blocs. The first bloc seeks to forecast the future value of GHI based on historical time series using the Long Short-Term Memory (LSTM) technique with two different search algorithms. In the second bloc, an appropriate separation (also referred to as “diffuse fraction” or “splitting”) model is implemented to extract the direct component of GHI. LSTMs constitute a category of recurrent neural network (RNN) structure that exhibits an excellent learning and predicting ability for data with time-series sequences [2]. The present study uses and evaluates the performance of two novel and competitive strategies, which both aim at providing accurate short-term GHI forecasts: Unidirectional LSTM (UniLSTM) and Bidirectional LSTM (BiLSTM). In the former case, the signal propagates backward or forward in time, whereas in the latter case the learning algorithm is fed with the GHI data once from beginning to the end and once from end to beginning. One goal of this study is to evaluate the overall advantages and performance of each strategy. Hence, this study aims to validate this new approach of obtaining 15- min DNI forecasts indirectly, using the most appropriate separation model. An important step here is to determine which model is suitable for the arid climate of Tamanrasset, a high-elevation site in southern Algeria where dust storms are frequent. Accordingly, four representative models have been selected here, based on their validation results [3] and popularity: 1) Erbs model [4]; 2) Maxwell’s DISC model [5]; 3) Perez’s DIRINT model [6]; and 4) Engerer2 model [7]. In this contribution, 1-min direct, diffuse and global solar irradiance measurements from the BSRN station of Tamanrasset are first quality-controlled with usual procedures [3, 8] and combined into 15-min sequences over the period 2013–2017. The four separation models are operated with the 15-min GHI forecasts obtained with each LSTM model, then compared to the 15-min measured DNI sequences. Table 1 shows the results obtained by the two forecasting strategies, for the experimental dataset.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadjidj, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Meriem Benbrahim</style></author><author><style face="normal" font="default" size="100%">Ounnes, D</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis and Design of Modified IncrementalConductance-BasedMPPT Algorithm for Photovoltaic System</style></title><secondary-title><style face="normal" font="default" size="100%">The First International Conference on Renewable  Energy Advanced Technologies and Applications (ICREATA’21 ), October 25-27</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA'21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">October 25-27</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Nowadays, solar energy, which is the direct conversion of light into electricity, occupies a very important place among renewable energy resources due to its daily availability in most regions of the globe. Therefore, the wise exploitation of this clean energy will ultimately drive to cover all needed demands [1, 2]. This paper deals with the design of Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) system using a modified incremental conductance (IncCond) algorithm to extract maximum power from PV module. The considered PV system consists of a PV module, a DC-DC converter and a resistive load. In the literature, it is known that the conventional MPPT algorithms suffer from serious disadvantages such as fluctuations around the MPP and slow tracking during a rapid change in atmospheric conditions. Therefore, in this paper, and attempting to overcome the shortcomings of conventional approach. In this work, a new modified incremental conductance algorithm is proposed to find the Maximum Power Point Tracking (MPPT) of the Photovoltaic System. Simulation tests with different atmospheric conditions are provided to demonstrate the validity and the effectiveness of the proposed algorithm.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zereg, Hadda</style></author><author><style face="normal" font="default" size="100%">Bouzgou, Hassen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Techno-Economic Analysis of a Stand-Alone Hybrid Renewable Energy System for Residentiel Electrification in Tamanrasset, Algeria</style></title><secondary-title><style face="normal" font="default" size="100%"> International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA'21)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%">Adrar, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Louchene, Houssem-Eddine</style></author><author><style face="normal" font="default" size="100%">Bouzgou, Hassen</style></author><author><style face="normal" font="default" size="100%">Gueymard, Chris</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Residual Networks with Long Short Term Memory for Hourly Solar Radiation Forecasting</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES'21) </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/355916572_Residual_Networks_with_Long_Short_Term_Memory_for_Hourly_Solar_Radiation_Forecasting</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Tipasa, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper describes a new approach for hourly global solar radiation forecasting based on a hybrid artificial neural network technique combining a residual neural network (RESNET) for powerful feature extraction of the most relevant moments of the past, and a long short-term memory (LSTM) technique for efficient projection into the future. Based on 11 years of solar irradiance measurements at Tamanrasset, Algeria, four evaluation metrics are used to demonstrate the efficiency of the proposed method: coefficient of determination (R²), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These metrics are also used to evaluate the performance of the model in comparison with two existing forecasting models used as benchmark: a particular technique of convolutional neural network (CNN) called 1-dimensional convolutional neural network (1D-CNN) and a conventional LSTM. The present results indicate that the proposed RESNET-LSTM model outperforms the other models in terms of all statistical indicators.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zereg, Hadda</style></author><author><style face="normal" font="default" size="100%">Bouzgou, Hassen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-Objective Optimization of Stand-Alone Hybrid Renewable Energy System for Rural Electrification in Algeria</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence in Renewable Energetic Systems(IC-AIRES'21 )</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-92038-8_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%"> Lecture Notes in Networks and Systems</style></publisher><pub-location><style face="normal" font="default" size="100%">Tipasa, Algeria  </style></pub-location><volume><style face="normal" font="default" size="100%">361</style></volume><pages><style face="normal" font="default" size="100%">21–33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71&amp;nbsp;$ and the COE equal to 0.79&amp;nbsp;$/kWh for an LPSP&amp;nbsp;=&amp;nbsp;0.01%, where the ten households are 0.99 % satisfied by renewable energy sources.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bellal, Salah-Eddine</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Sahnoun, M’hammed</style></author><author><style face="normal" font="default" size="100%">Messaadia, Mourad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cost Optimisation for Wheelchair Redesign</style></title><secondary-title><style face="normal" font="default" size="100%">1st International Conference On Cyber Management And Engineering (CyMaEn), 26-28 May</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9497281</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Hammamet, Tunisia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don’t meet international standards. However, developing countries are obliged to purchase products from developed countries that don’t necessarily meet all user’s requirements. The modification of these requirements will generate disruption on all the supply chain. This paper proposes a model for optimising the cost of requirement modification on the supply chain and seeks to evaluate the introduction of a new requirement on an existing product/process. This model is adapted to the redesign and development of products, such as wheelchairs, satisfying specific Algerian end-user requirements.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadjidj, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Meriem Benbrahim</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study Between Data-Based Approaches Under Earlier Failure Detection</style></title><secondary-title><style face="normal" font="default" size="100%">ICCIS2020 </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-981-16-1089-9_20</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lecture Notes in Networks and Systems</style></publisher><pub-location><style face="normal" font="default" size="100%">India</style></pub-location><volume><style face="normal" font="default" size="100%">204</style></volume><pages><style face="normal" font="default" size="100%">235–239</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author><author><style face="normal" font="default" size="100%">Djurović, Siniša</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning for Photovoltaic Systems Condition Monitoring: A Review</style></title><secondary-title><style face="normal" font="default" size="100%">47th Annual Conference of the IEEE Industrial Electronics Society, IECON </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9589423</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Toronto, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Leïla-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning</style></title><secondary-title><style face="normal" font="default" size="100%">47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9589114</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Toronto, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Principles of Biology in Service of Technology: DNA Computing</style></title><secondary-title><style face="normal" font="default" size="100%">Algerian Journal of Environmental Science and Technology (ALJEST)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.aljest.net/index.php/aljest/article/view/292</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;em&gt;&amp;nbsp;&lt;/em&gt;&lt;em&gt;As commonly known that living beings cannot survive without natural sources available on earth, technology is no exception; it cannot develop without the inspiring help given by the same nature.&lt;/em&gt;
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;em&gt;The field of biology has extensively participated in the computing field through the &quot;code of life&quot; DNA (Deoxyribo Nucleic Acid) since it was discovered by Adelman in the past century. This combination gave birth to DNA Computing, which is a very interesting new aspect of biochemistry. It works massively parallel with high energy efficiency, and requiring almost no space.&lt;/em&gt;
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;em&gt;The field of molecular computing is still new and as the field progresses from concepts to engineering, researchers will address these important issues.&lt;/em&gt;
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;em&gt;&amp;nbsp;By the use of encoding data into DNA strands, many NP-complete problems have been solved and many new efficient techniques have been proposed in cryptography field.&lt;/em&gt;
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	&lt;em&gt;The aim of this paper is to give an overview of bio-inspired system and to summarize the great role of DNA molecule in servicing of the technology field.&lt;/em&gt;
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">20</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bensakhria, Mohamed</style></author><author><style face="normal" font="default" size="100%">Samir Abdelhamid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Hybrid Methodology based on heuristic algorithms for a production distribution system with routing decisions</style></title><secondary-title><style face="normal" font="default" size="100%">. BizInfo (Blace) Journal of Economics, Management and Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://scindeks-clanci.ceon.rs/data/pdf/2217-2769/2021/2217-27692102001B.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">1-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, we address the integration of a two-level supply chain with multiple items. This two-level production-distribution system features a capacitated production facility supplying several retailers located in the same region. If production does occur, this process incurs a fixed setup cost and unit production costs. Besides, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles, routing costs incurred. This work aims to implement a minimization solution that reduces the total costs in both the production facility and retailers. The methodology adopted based on a hybrid heuristic, greedy and genetic algorithm uses strong formulation to provide a suitable solution of a guaranteed quality that is as good or better than those provided by the MIP optimizer. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benfriha, Abdennour -Ilyas</style></author><author><style face="normal" font="default" size="100%">Triqui-Sari, Lamia</style></author><author><style face="normal" font="default" size="100%">Bougloula, Aimade-Eddine</style></author><author><style face="normal" font="default" size="100%">BENNEKROUF, Mohammed</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic planning design of three level distribution network with horizontal and vertical exchange</style></title><secondary-title><style face="normal" font="default" size="100%"> Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central ware</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	&amp;nbsp;Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author><author><style face="normal" font="default" size="100%">BOUHAFNA, Khayreddine</style></author><author><style face="normal" font="default" size="100%">BELAYATI, Souleymen</style></author><author><style face="normal" font="default" size="100%">DJEGHAR, Dina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vers une Nouvelle Révolution Industrielle : Industrie 4.0</style></title><secondary-title><style face="normal" font="default" size="100%">Revue Méditerranéenne des Télécommunications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://revues.imist.ma/index.php/RMT/article/view/24027</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	La quatrième révolution industrielle (nommée aussi l’Internet Industriel des Objets) dépend totalement sur la numérisation à travers l’Internet des objets et les réseaux virtuels. Cette révolution qui évolue à un rythme exponentiel, et non plus linéaire, va permettre la création d’usines, d’industries et de processus plus intelligents qui vont ensuite se traduire par une amélioration de la flexibilité, de la productivité et une meilleure utilisation des ressources matérielles et humaines.
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	Cet article est consacré à introduire cette nouvelle révolution industrielle (industrie4.0), les technologies majeurs participant à son apparition, leur bénéfices attendus ainsi que leurs enjeux à prendre en considération.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author><author><style face="normal" font="default" size="100%">Samia Aitouche</style></author><author><style face="normal" font="default" size="100%">Bentoumi, Hamza</style></author><author><style face="normal" font="default" size="100%">Sersa,  Ibrahim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Developing a Web Platform for the Management of the Predictive Maintenance in Smart Factories</style></title><secondary-title><style face="normal" font="default" size="100%">Wireless Personal Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s11277-021-08290-w</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">119</style></volume><pages><style face="normal" font="default" size="100%">pages1469–1497</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Sarma, Nur</style></author><author><style face="normal" font="default" size="100%">Djurović, Siniša</style></author><author><style face="normal" font="default" size="100%">Wu, Yueqi</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review</style></title><secondary-title><style face="normal" font="default" size="100%">Energies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Leïla-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Toufik Bentrcia</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Instrumentation and Measurement (2022)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9552475</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">37</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Al-Masry, Zeina</style></author><author><style face="normal" font="default" size="100%">Terrissa, Labib</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Digital Twins Driven Breast Cancer Detection</style></title><secondary-title><style face="normal" font="default" size="100%"> Lecture Notes in Networks and Systems </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">285</style></volume><pages><style face="normal" font="default" size="100%">87–99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Leïla-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">Energies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/1996-1073/14/8/2163</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Seddik, Mohamed-Takieddine</style></author><author><style face="normal" font="default" size="100%">Ouahab Kadri</style></author><author><style face="normal" font="default" size="100%">Bouarouguene, Chakir</style></author><author><style face="normal" font="default" size="100%">Brahimi, Houssem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of Flooding Attack on OBS Network Using Ant Colony Optimization and Machine Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Computación y Sistemas</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462021000200423</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Muyeen, S-M</style></author><author><style face="normal" font="default" size="100%">Toufik Bentrcia</style></author><author><style face="normal" font="default" size="100%">Leïla-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9610082</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zuluaga-Gomez, J</style></author><author><style face="normal" font="default" size="100%">Al Masry, Z</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A CNN-based methodology for breast cancer diagnosis using thermal images</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/abs/10.1080/21681163.2020.1824685</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">131-145</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs
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</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gougam, Fawzi</style></author><author><style face="normal" font="default" size="100%">Chemseddine, Rahmoune</style></author><author><style face="normal" font="default" size="100%">Benazzouz, Djamel</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.sagepub.com/doi/abs/10.1177/0954406220976154</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">235</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.
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</style></abstract><issue><style face="normal" font="default" size="100%">20</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Derdour, Khedidja</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Rafik Bensaadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition</style></title><secondary-title><style face="normal" font="default" size="100%"> International Journal on Electrical Engineering and Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.proquest.com/openview/72acbcf9bceca23bc38c7d1df8734acb/1?pq-origsite=gscholar&amp;cbl=316223</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, we present a system for handwriting digit recognition using different invariant features extraction and multiple classifiers. In the feature extraction we use four types: cavities, Zernike moments, Hu moments, Histogram of Gradient (HOG). Firstly, the features are used independently by five classifiers: K-nearest neighbor (KNN), Support Vector Machines (SVM) one versus one, SVM one versus all, Decision Tree, MLP. Then to achieve the best possible classification performance in terms of recognition rate, three methods of classifiers Combination rule employed: majority vote, Borda count and maximum rule. Experiments are performed on the well-known MNIST database of handwritten digits. The results demonstrated that the combination of KNN using HOG features with SVMOVA using Zernike moments by Borda count rule have considered to be good based on a geometric transformation invariance.
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</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Sarma, Nur</style></author><author><style face="normal" font="default" size="100%">Djurović, Siniša</style></author><author><style face="normal" font="default" size="100%">Wu, Yueqi</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review</style></title><secondary-title><style face="normal" font="default" size="100%">Energies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ideas.repec.org/a/gam/jeners/v14y2021i18p5967-d639498.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.
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</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Toufik Bentrcia</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author><author><style face="normal" font="default" size="100%">Djurović, Siniša</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects</style></title><secondary-title><style face="normal" font="default" size="100%">Energies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/1996-1073/14/19/6316</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.
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</style></abstract><issue><style face="normal" font="default" size="100%">19</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ayoub Benayache</style></author><author><style face="normal" font="default" size="100%">Azeddine Bilami</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Industrial IoT middleware using a multi-agent system for consistency-based diagnostic in cement factory</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Autonomous and Adaptive Communications Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.inderscienceonline.com/doi/abs/10.1504/IJAACS.2021.117805</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	With the evolution of the internet of things (IoT), and due to its significant need in the industry, Industrial IoT (IIoT) becomes the suitable naming for this accompaniment. IIoT changed the view of the industry intelligently and over the internet. This overlapping of IoT and industry requires special treatment when systems deal with heterogeneous devices in a distributed environment and complex tasks. In this paper, we propose a middleware solution based on multi-agents system (MAS) to handle the distributed control of complex systems autonomously in an industrial environment. The proposed middleware enables machine-to-machine (M2M) communications among the system's components. In this work, we also addressed the distributed diagnostic for real industrial system using MAS with a new suitable communication strategy to support the heterogeneity and interoperability issued in IIoT and assure real-time monitoring and control. Finally, we present a qualitative evaluation of our solution on real case study (cement factory).
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</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">Toufik Bentrcia</style></author><author><style face="normal" font="default" size="100%">Elbouchikhi, Elhoussin</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A deep supervised learning approach for condition-based maintenance of naval propulsion systems</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">221</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p id=&quot;abspara0010&quot; style=&quot;text-align: justify;&quot;&gt;
	In the last years,&amp;nbsp;predictive maintenance&amp;nbsp;has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of&amp;nbsp;extreme learning machines&amp;nbsp;using data issued from a&amp;nbsp;propulsion system&amp;nbsp;simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.
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</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ouahiba Chouhal</style></author><author><style face="normal" font="default" size="100%">Rafik Mahdaoui</style></author><author><style face="normal" font="default" size="100%">Leila-Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SOA-based distributed fault prognostic and diagnosis framework: an application for preheater cement cyclones</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Internet Manufacturing and Services</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.inderscienceonline.com/doi/abs/10.1504/IJIMS.2021.114539</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Complex engineering manufacturing systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralised, but these solutions are difficult to implement on distributed systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, controlling process plant from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and service-oriented architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for web service-based distributed fault prognostic and diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ag Hameyni, Abdoulmadjid</style></author><author><style face="normal" font="default" size="100%">Samia Aitouche</style></author><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proposition d&amp;rsquo;un tutoriel pour une usine apprenante (Textile de Batna)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><edition><style face="normal" font="default" size="100%">universitaires europeennes</style></edition><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Le contexte actuel de globalisation et de concurrence accrue a entrainé les firmes à reconsidérer leurs stratégies d’internationalisation et à réexaminer les opportunités offertes et les risques associés. Les alliances stratégiques apparaissent ainsi comme des vecteurs privilégiés notamment par les firmes multinationales pour leurs nouvelles implantations.La communication et le travail d’équipe sont parmi les compétences les plus récurrents associés à une connaissance des sciences de l’ingénieur. Cependant, leur application n’est pas simple, en raison de l’absence d’approche pédagogique contribuant à développer des connaissances fondées sur l’expérience.Dans ce travail nous avons défini qu’est-ce qu’une organisation apprenante, qu’est-ce qu’un tutoriel et pourquoi un tutoriel personnalisé dans un métier, ses différentes formes et les démarches pour l’élaboration d’un tutoriel.Après nous avons donné une présentation de l’entreprise qu’est Textile Batna. Nous avons conçu un tutoriel pour l’entreprise sous forme d’un site Web. Pour cela, le langage UML a été utilisé. Les fonctionnalités du tutoriel ont été présentées.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recherche Documentaire et Conception du Mémoire</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Le 4ème semestre d’un mastère de recherche est consacré à la réalisation d’un travail de recherche qui sera traduit par une conception et une rédaction d'un mémoire de fin d'études et finalement la préparation d'un exposé oral puis une soutenance.Le mémoire de fin d’études est une étape très importante dans la voie des études universitaires, car sans elle, l'étudiant ne peut pas acquérir la qualité de diplômé.Alors, dans ce petit livre vous pouvez trouver un petit guide sur: - La façon d'organisation de votre mémoire. - La présentation de votre soutenance. - La rédaction d'un travail de recherche. - La préparation d'un poster.Le 4ème semestre d’un mastère de recherche est consacré à la réalisation d’un travail de recherche qui sera traduit par une conception et une rédaction d'un mémoire de fin d'études et finalement la préparation d'un exposé oral puis une soutenance.Le mémoire de fin d’études est une étape très importante dans la voie des études universitaires, car sans elle, l'étudiant ne peut pas acquérir la qualité de diplômé.Alors, dans ce petit livre vous pouvez trouver un petit guide sur:
&lt;/p&gt;

&lt;ul&gt;
	&lt;li style=&quot;text-align: justify;&quot;&gt;
		La façon d'organisation de votre mémoire.
	&lt;/li&gt;
	&lt;li style=&quot;text-align: justify;&quot;&gt;
		La présentation de votre soutenance.
	&lt;/li&gt;
	&lt;li style=&quot;text-align: justify;&quot;&gt;
		La rédaction d'un travail de recherche.
	&lt;/li&gt;
	&lt;li style=&quot;text-align: justify;&quot;&gt;
		La préparation d'un poster.
	&lt;/li&gt;
&lt;/ul&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aksa,  Karima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CAPTEURS INTELLIGENTS</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><edition><style face="normal" font="default" size="100%">Bookelis </style></edition><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	L'évolution récente des moyens de la communication sans fil a permet la manipulation de l'information à travers des unités de calculs portables, appelés capteurs. Ces derniers, qui ont des caractéristiques particulières, sont capables de récolter, de traiter et de transmettre des données environnementales d'une manière autonome.&lt;br&gt;&lt;br&gt;Dans ce livre sont introduites les connaissances de base nécessaires à la bonne compréhension des capteurs intelligents, des réseaux de capteurs et les différents types protocoles de routage spécifiques aux réseaux de capteurs. Nous fournirons ainsi les définitions généralement acceptées par ce type de réseau. Nous aborderons également par une description succincte les principales caractéristiques, contraintes et facteurs conceptuels qui surviennent dans ces réseaux. Nous présenterons ensuite les différentes orientations prises aux applications des réseaux de capteurs.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haoues, Mohamed</style></author><author><style face="normal" font="default" size="100%">Dahane, Mohammed</style></author><author><style face="normal" font="default" size="100%">Mouss, Nadia-Kenza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Capacity Planning With Outsourcing Opportunities Under Reliability And Maintenance Constraints. Status</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Industrial and Systems Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ideas.repec.org/a/ids/ijisen/v37y2021i3p382-409.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">382-409</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer's company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>